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✨Data Intelligence Lab@HKU✨ avatar

LightRAG

Graph-based retrieval-augmented generation library with a web UI

  • Agent frameworks and tools
  • AI models and runtimes
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What it is

LightRAG is a Python framework that builds a knowledge graph from your documents and combines graph and vector retrieval to answer questions with an LLM. It supports storage backends such as PostgreSQL, Neo4j, MongoDB and OpenSearch, parses PDFs and Office files through MinerU or Docling, and includes a server with a web interface for inserting, querying and visualizing data. Developers building question-answering systems over their own document collections use it as a lighter alternative to heavier GraphRAG pipelines, with RAGAS evaluation and Langfuse tracing built in.

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Facts

Stars
40,240 GitHub stars.
40,240

Forks
5,710
Commits
10,873
Contributors
315
Language
Python
License
MIT
Last commit
Oct 14, 2026
On GitHub since
October 2024
First release
December 2024
Platforms
web, self-hosted, library
Self-hostable
Yes
Tags
  • semantic-search
  • document-parsing
  • llm-evaluation
  • rag
  • knowledge-graph

Last synced from GitHub on Oct 20, 2026

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What people are saying

Updated Oct 21, 2026
  • Bluesky“Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, Chao Huang LightRAG: Simple and Fast Retrieval-Augmented Generation”Oct 10, 2024 · 0 likes · 0 replies (opens Bluesky in a new tab)